June 11	June 12	June 13	June 14
Morning	Andrea Fusiello	Tanya Birch	Hannah Kerner	Zan Gojcic
Afternoon	Dalton Lunga	-	Konstantinos Karantzalos	-

Title: Graph Synchronization and Rigidity: Unraveling the Theory Underneath Structure from Motion

Abstract: In this talk, I will explore synchronization problems within graph theory, focusing on motion synchronization and its connection to Structure from Motion (SFM). The goal is to establish consistent orientations for a set of sensors based on relative motion estimates, addressing the challenges of noisy edge measurements. I will also touch on related localization issues, where unknown sensor positions and measurements such as distances or directions contribute to the complexity of the problem. The talk serves as a concise guide to understanding these intricacies, emphasizing the interplay between synchronization, SFM, and rigidity in computer vision and photogrammetry.

Bio: Andrea Fusiello earned his Laurea (M.S.) degree in computer science from the University of Udine (Italy) in 1994 and later completed his PhD in computer engineering from the University of Trieste in 1999. Following this, he served as a Research Fellow at Heriot-Watt University, Edinburgh, in 1999. From 2001 to 2011, he contributed significantly to the Department of Computer Science at the University of Verona. As an Associate Professor, he joined the DPIA at the University of Udine in 2012, ultimately achieving the position of Full Professor in 2023. With over 150 published articles and holding two patents, Andrea is a distinguished researcher. His broad research interests span various domains in Computer Vision, Photogrammetry, and Image Analysis, with a specific focus on 3-D modeling and reconstruction. He was honored with the Marr Prize - Honorable Mention in 2021.

Title: The Role of AI and Earth Observation in Safeguarding Society and Economic Stability

Abstract: Accurate prediction of extreme weather, climate events, and monitoring of man-made hazards, is crucial for safeguarding society and protecting economic stability. Artificial intelligence and Earth observation are emerging as transformational technologies enabling timely preparedness, mitigation, response, and recovery strategies. This discussion will share example impacts as the frequency, intensity, and durations of armed conflicts and natural disasters—wars, flooding, tornadoes, hurricanes—are increasing under a changing climate and geopolitical instabilities, bringing unprecedented destruction to property and lives. We will talk about the growing need to develop capable Earth observation-based foundation models. Touching on the current challenges, gaps, and how Oak Ridge National Laboratory is leading the charge in establishing best practices in scaling and training energy efficient foundation models. Lastly, as the advancements toward multimodal Earth and climate science foundation models for safeguarding society bring communities together, we look at – what role can the photogrammetry and remote sensing communities play?

Bio: Dalton is a group leader for GeoAI and a senior R&D staff scientist at ORNL. He is an Associate Editor for Geoscience and Remote Sensing Letters. Dalton is an interdisciplinary scientist with expertise in artificial intelligence, computer vision, high-performance computing and remote sensing. He leads multidisciplinary teams and projects focused on developing novel methods at the intersection of AI, computer vision, and geography toward the built and physical environment mapping using earth observation data. His research is impacting the development of accurate population estimates, informing disaster response, identifying at-risk areas to support national security operations missions. Prior to ORNL, Dalton was a Team Lead and Senior Research Scientist at the Council for Scientific and Industrial Research, South Africa where he established and led a Data Science for Decision Impact team. He received his Ph.D. in Electrical and Computer Engineering from Purdue University, West Lafayette.

Title: Pixels with Purpose: Illuminating the Path to Change

Bio: Tanya Birch is a Senior Program Manager at Google, using Google's mapping technology, AI capabilities and Cloud platform for helping monitor ecosystems and biodiversity. She leads the Forest & Nature program within Geo Sustainability, which catalyzes positive environmental impact at scale using Google’s understanding of the real world. She works with leading public & private sector organizations in applying technology to address nature-based solutions to climate change. She led the program management of Dynamic World, a novel deep learning approach to land cover mapping with World Resources Institute, and is founding technology partner of Wildlife Insights, a global platform for biodiversity monitoring with 7 leading conservation organizations. Prior to Google, she researched and mapped human elephant conflict with the Sri Lanka Wildlife Conservation Society. She holds a BA in Geography and Environmental Studies from the University of California at Santa Barbara.

Title: Unlocking the Potential of Planetary-Scale Machine Learning for a Sustainable Future

Abstract: Remote sensing satellites capture peta-scale, multi-modal data capturing our dynamic planet across space, time, and spectrum. This rich data source holds immense potential for addressing local and planetary-scale challenges including food insecurity, poverty, climate change, and ecosystem preservation. Fully realizing this potential will require a new paradigm of machine learning approaches capable of tackling the unique character of remote sensing data. Machine learning approaches must be flexible enough to make use of the multi-modal multi-fidelity satellite data, process meter-scale observations over planetary scales, and generalize to the challenging diversity of remote sensing tasks. In this talk, I will present examples of how we are developing machine learning approaches for planetary data processing including self-supervised transformers for remote sensing data. I will also demonstrate how treating ML research and deployment as a unified approach instead of siloed steps leads to research advances that result in immediate societal impact, highlighting examples of how we are partnering directly with stakeholders to deploy our innovations in areas of critical need across the globe.

Bio: Hannah Kerner is an Assistant Professor of computer science in the School of Computing and Augmented Intelligence at Arizona State University. She is pioneering new machine learning techniques to harness the potential of remote sensing data to address global challenges like food insecurity and climate change. Her research aims to tackle barriers to realizing the benefits of machine learning in real-world applications that benefit society. As the AI Lead for NASA's agriculture programs, NASA Harvest and NASA Acres, she is deploying research methods in real applications across the globe; her projects have directly resulted in optimized agricultural planning, disaster response, and financial relief in various regions around the world. The impact of Kerner’s research was recognized in Forbes 30 Under 30 and the International Research Centre On Artificial Intelligence's Top 10 projects solving problems related to the UN's Sustainable Development Goals with AI.

Abstract: Nowadays, the marine environment concentrates significant amount of research and development aiming to map and monitor the sea surface, the water column, and the seabed with the same quality and efficiency as in land. Multimodal data, with increased spatial, spectral, temporal resolution acquired from different payloads onboard satellites, aerial, marine and submarine robots are delivering valuable information that should be exploited in an automated manner. In this talk, we will discuss cutting-edge sensing technologies, AI, ML, and data cloud/edge analytics that can harmonize observations, understand them, and monitor effectively the dynamic marine environment.

Bio: Dr. Karantzalos is a Professor at the National Technical University of Athens, joining the Remote Sensing Laboratory. His teaching and research interests include earth observation and remote sensing, geospatial big data and analytics, computer vision and machine learning. He has numerous publications in international journals and conferences and a number of awards and honors for his research contributions. He has more than 20 years of research experience, involved with more than 30 EU and national excellence/ competitive research projects. He is currently the Head of the Greek delegation in the European Space Agency and coordinates the design and implementation of the Greek small satellites program.

Abstract: Neural radiance fields have emerged as a powerful 3D representation, enabling photorealistic novel-view synthesis and reconstruction. However, the high visual fidelity comes at a high computational cost, as the volume rendering formulation requires many samples along each ray. On the other hand, surface-based rendering methods are very efficient to render but may lack high visual fidelity, especially in regions that are more fuzzy and volumetric. Recently, several works in the field of text-to-3D have shown how the two can be combined into a two-stage pipeline that yields improved visual fidelity and high-resolution details. In this talk, I will allude to further possibilities of combining the two formulations in order to get the best of both worlds.

Bio: Zan Gojcic is a senior research scientist and a research manager at the Nvidia Toronto Ai Lab, where his team is exploring topics related to neural reconstruction and data driven simulation. Prior to joining Nvidia, Zan has received his PhD from ETH Zurich. His thesis titled “Benefiting from local rigidity in 3D point cloud processing” was awarded ETH Medal - an award bestowed upon outstanding doctoral theses. During his PhD, Zan has also visited the geometric computation group led by Leonidas Guibas at Stanford University. His research interests are focused on 3D neural reconstruction, generative models, and their combination.